{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "# import seaborn as sns\n",
    "# import matplotlib.pyplot as plt\n",
    "import os\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "from feature_engineering import transformation\n",
    "# plt.style.use('seaborn-colorblind')\n",
    "# %matplotlib inline\n",
    "#from feature_cleaning import rare_values as ra"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "use_cols = [\n",
    "    'Pclass', 'Sex', 'Age', 'Fare', 'SibSp',\n",
    "    'Survived'\n",
    "]\n",
    "\n",
    "data = pd.read_csv('./data/titanic.csv', usecols=use_cols)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>7.2500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>71.2833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass     Sex   Age  SibSp     Fare\n",
       "0         0       3    male  22.0      1   7.2500\n",
       "1         1       1  female  38.0      1  71.2833\n",
       "2         1       3  female  26.0      0   7.9250"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((623, 6), (268, 6))"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Note that we include target variable in the X_train \n",
    "# because we need it to supervise our discretization\n",
    "# this is not the standard way of using train-test-split\n",
    "X_train, X_test, y_train, y_test = train_test_split(data, data.Survived, test_size=0.3,\n",
    "                                                    random_state=0)\n",
    "X_train.shape, X_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Logarithmic transformation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Variable Fare Q-Q plot\n"
     ]
    },
    {
     "data": {
      "image/png": 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4K/ATYFzHly1JkiSp0kyalC3J1tjy5dn2Nvn1r2G77bJ13W68MRv3tsEG7a6zmrXaby2l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\ngg==\n",
      "text/plain": [
       "<Figure size 1080x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     Survived  Pclass     Sex   Age  SibSp     Fare  Fare_log\n",
      "857         1       1    male  51.0      0  26.5500  3.316003\n",
      "52          1       1  female  49.0      1  76.7292  4.353231\n",
      "386         0       3    male   1.0      5  46.9000  3.869116\n",
      "124         0       1    male  54.0      0  77.2875  4.360388\n",
      "578         0       3  female   NaN      1  14.4583  2.738146\n",
      "549         1       2    male   8.0      1  36.7500  3.630985\n"
     ]
    }
   ],
   "source": [
    "# add the new created feature\n",
    "X_train_copy = X_train.copy(deep=True)\n",
    "X_train_copy = transformation.log_transform(data=X_train,cols=['Fare'])\n",
    "print(X_train_copy.head(6))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reciprocal transformation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Variable Fare Q-Q plot\n"
     ]
    },
    {
     "data": {
      "image/png": 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z88OZuaIG590IfJmi+XoYuCYzH4yIsyLiUICI+EBErAaOAH4YEQ+Wjl0LnE3R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\nNeJGnJmZmZmZWY38H+iwR1dz0ksKAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1080x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     Survived  Pclass     Sex   Age  SibSp     Fare  Fare_reciprocal\n",
      "857         1       1    male  51.0      0  26.5500         0.037665\n",
      "52          1       1  female  49.0      1  76.7292         0.013033\n",
      "386         0       3    male   1.0      5  46.9000         0.021322\n",
      "124         0       1    male  54.0      0  77.2875         0.012939\n",
      "578         0       3  female   NaN      1  14.4583         0.069164\n",
      "549         1       2    male   8.0      1  36.7500         0.027211\n"
     ]
    }
   ],
   "source": [
    "# add the new created feature\n",
    "X_train_copy = X_train.copy(deep=True)\n",
    "X_train_copy = X_train_copy[X_train_copy.Fare!=0] # Warning that x should not be 0\n",
    "#X_train_copy[X_train_copy.Fare==0]\n",
    "X_train_copy = transformation.reciprocal_transform(data=X_train_copy,cols=['Fare'])\n",
    "print(X_train_copy.head(6))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Square root transformation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Variable Fare Q-Q plot\n"
     ]
    },
    {
     "data": {
      "image/png": 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E3Jfbd3lK6Re1D46IgcDRwCBgS+BvEbFtSmlNMQuXJEmS1L61x0lIWqrJHriU0usppSdy\nn98FXgC2auSUw4BbUkofppT+B5gH7FGMYiVJqgQRcUNELImIZ2u1NThyRZI6utrrvW2xRfZab72s\nraqq1NWVl4KegYuIfsCuwKO5plMj4uncL6rNcm1bAa/UOu1VGg98kiS1N78DDqin/fKU0i65111t\nXJMklaX61nurWfNt4cJsnyHuE3mvAxcRGwN/BH6UUloeEdcAFwAp9z4J+D4Q9Zye6rneSGAkQM+e\nPamuri64+NpWrFjR4msUy9gdV5e6hI81dk/K6Z5VAu9X4bxnhfF+tR8ppQdz/+gpSWpAVVW21tvC\nhY0ft3JldpxDKTN5BbiI6EwW3qpSSn8CSCm9UWv/tcBfcpuvAr1rnb41sLjuNVNKU4ApAIMHD05D\nhgxpRvmfqK6upqXXKJYRZ91Z6hI+9vKwIQ3uK6d7Vgm8X4XznhXG+9UhnBoR3wNmkT1f/napC5Kk\nUqjpdau7UHdDOvrab7XlMwtlANcDL6SUJtdq71XrsG8DNeP8ZwBHR8QGEfEFYADwz+KVLElSRboG\n2AbYBXidbORKvSJiZETMiohZS5cubav6JKnNjB+ff3gD136rLZ8euK8CxwLPRMTsXNvZwDERsQvZ\n8MiXgZMBUkrPRcR04HmyGSxPcQZKSVJH18jIlfqO/dQoldavTpLaViE9aq799mlNBriU0kPU/1xb\ngw9fp5QmAt5mSZJyIqJXSun13GbtkSuS1C7VfsatUydYs+aT98Z07569L1uW9bxNnOjzb7XlPYmJ\nJEnKT0TcDAwBtoiIV4EJwJD6Rq5IUntU9xm3mtDWWHjr2tWFuvNhgJMkqchSSsfU03x9mxciSSVS\n6DNunToZ3vJV0DpwkiRJktSUQmeNXLvW8JYvA5wkSZKkoip01khnmcyfAU6SJElSUU2cmD3Tlg9n\nmSyMAU6SJElSUQ0blj3T1rdvtt2pU/3vffv67FuhnMREkiRJUtENG2Ywaw32wEmSJElShTDASZIk\nSVKFMMBJkiRJUoUwwEmSJElShTDASZIkSVKFMMBJkiRJUoUwwEmSJElShTDASZIkSVKFMMBJkiRJ\nUoUwwEmSJElShTDASZIkSVKFMMBJkiRJUoUwwEmSlIeI2Cwidip1HZJU7qqqoF8/WG+97L2qqtQV\ntS/rl7oASZLKVURUA4eS/b6cDSyNiP+bUjq9pIVJUpmqqoKRI2Hlymx74cJsG2DYsNLV1Z7YAydJ\nUsM+m1JaDnwH+G1KaTdgnxLXJElla/z4T8JbjZUrs/Z26/HHs1cbMcBJktSw9SOiF/AfwF9KXYwk\nlbtFiwprr2hz5sBRR8HgwXDOOW32tQY4SZIa9jPgXmB+SumxiOgPzC1xTZJUtvr0Kay9Ii1aBCec\nAIMGwd13w09/Crfc0mZf7zNwkiQ1IKX0e+D3tbYXAEeUriJJKk9VVTBmDLz11rr7unaFiRPbvqai\nW7oULr4Yfv3rbPu00+Dss+Fzn2vTMuyBkySpARGxbUTcHxHP5rZ3ioi2GycjSWWoZpbJiGymyQgY\nPrz+8Na9O0yZUuETmCxfDuedB/37wxVXZD/MSy/BL3/Z5uENDHCSJDXmWmAcsAogpfQ0cHRJK5Kk\nEqqZZXLhwmw7pcaP33jjCg5vH3wAkydnwe3882G//eDZZ+GGG6Bv35KVZYCTJKlhXVNK/6zTtrok\nlUhSGRgzZt1ZJhtTkZOXrF4N110HAwbA2LGw667wz3/CH/8IO+xQ6uoMcJIkNeLNiNgGSAARcSTw\nemlLkqTSqKqqf5hkYypq8pK1a2H69GxykpNOgi23hPvvh/vug913L3V1H3MSE0mSGnYKMAXYPiJe\nA/4HGF7akiSpNApdy61LlwqZvCQluPfebEKSJ5+EgQPhttvgsMOyB/zKjAFOkqQG5Gad3CciugHr\npZTeLXVNklQqhQyH7N79k/k+ytojj8C4cfDgg9nMLFOnZkV36lTqyhpkgJMkqQERcW6dbQBSSj8r\nSUGSVEJ9+nwyeUl9Kia0ATzzTNal+Oc/Q8+ecOWV2bDJDTYodWVN8hk4SZIa9l6t1xrgQKBfKQuS\npFKZODFb0622rl1h2rRsFOKbb1ZAeFuwIFvzYOeds163iRNh/nw49dSKCG9gD5wkSQ1KKU2qvR0R\nvwBmlKgcSWpTjS3ODRXW4/b663DBBXDttbD++vCTn8CZZ8Lmm5e6soIZ4CRJyl9XoH+pi5Ck1lRV\nBSefDO+91/hx71bCU8Fvvw2XXgq/+hWsWgUnngg//Wk2w2SFMsBJktSAiHiG3BICQCegB+Dzb5La\nraoqOP74LOs05aOPssfIyrIH7r33stB22WXwzjtwzDHZYtxf/GKpK2uxJgNcRPQGbgQ+D6wFpqSU\nroiIzYFbyZ4FeBn4j5TS25E94X0FcBCwEhiRUnqidcqXJKlVHVzr82rgjZSSC3lLarfGj88vvNUo\nu4W6P/ooGyZ5wQXwxhtw8MHZc2477VTqyoomn0lMVgNjU0o7AHsCp0TEQOAs4P6U0gDg/tw2ZA94\nD8i9RgLXFL1qSZJaUURsnvuHyndrvd4HNsm1S1K7VGggK5uFutesgZtugu23zyYk2XZbeOihbJbJ\ndhTeII8euJTS68Druc/vRsQLwFbAYcCQ3GFTgWrgzFz7jSmlBPwjIjaNiF6560iSVAkeJxs6Wd8K\nrgmfg5PUTjW1VEBtZbFQd0owYwaccw48+yzssgvcdRcccEBZLsJdDAUtIxAR/YBdgUeBnjWhLPf+\nudxhWwGv1Drt1VybJEkVIaX0hZRS/9x73ZfhTVK7NXEidO7c9HHdu8MNN5T4+beZM2GvveDww+HD\nD+GWW+Dxx+HAA9tteIMCJjGJiI2BPwI/Siktj4ZvSkP/Wln3eiPJhljSs2dPqqur8y2lXitWrGjx\nNYpl7I7l83hEY/eknO5ZJfB+Fc57VhjvV3mKiM3IHgvYsKYtpfRg6SqSpNZTE8hqLx9QdssFzJoF\nZ58N990HW20FU6bAiBH5Jc92IK8AFxGdycJbVUrpT7nmN2qGRkZEL2BJrv1VoHet07cGFte9Zkpp\nCjAFYPDgwWnIkCHN+wlyqquraek1imXEWXeWuoSPvTxsSIP7yumeVQLvV+G8Z4XxfpWfiDgRGEP2\nu2w22bPgfwe+Wcq6JKnY6lvzreyC25w52RIAf/hDVtwvfgGjR8NGG5W6sjbV5BDK3KyS1wMvpJQm\n19o1Azgu9/k44I5a7d+LzJ7AOz7/JkmqUGOA3YGFKaVvkD1GsLS0JUlScdUsHVB3we633oLvfz/b\nX1KLFsEJJ8CgQXDPPXDuubBgAYwd2+HCG+T3DNxXgWOBb0bE7NzrIOASYN+ImAvsm9sGuAtYAMwD\nrgVGF79sSZLaxAcppQ8AImKDlNIcYLsS1yRJRdXY0gE1a72VxNKl8OMfw4ABMG0anHYazJ+free2\nySYlKqr08pmF8iHqf64NYGg9xyfglBbWJUlSOXg1IjYFbgfui4i3qeexAEmqVFVVTc862eZrvS1f\nDpMmweTJsHIlHHccTJgAffu2cSHlKe9JTCRJ6mhSSt/OfTwvImYCnwXuKWFJklQ0NUMnm9Jma719\n8AFcfTVcdFE2fvOII7IFuXfYoY0KqAwFLSMgSVJHEBF3RsSwiOhW05ZS+r8ppRkppY9KWZskFcuY\nMQ0PnazRJmu9rV4N112XDZUcOxa+/GV47LFsshLD2zoMcJIkrWsKcDDwckTcGhGHR0SXUhclScUy\nevS6k5bU1eprva1dC9OnZ5OTnHQSbLkl3H8//PWvMHhwK31p5XMIpSRJdaSU7gDuiIiNgEPJZlv+\nTUTcBdycUrqvpAVKUjNVVcHJJ8N77zV+XN++8PLLrVRESnDvvdlabk8+CQMHwm23wWGHtesFuIvF\nHjhJkhqQUno/pXRr7lm4/ciWEfAZOEkVqeaZt6bCG7TisMlHHoEhQ+DAA+Htt2HqVHj6aTj8cMNb\nngxwkiQ1ICJ6RsRpEfEw2UyUfwV2K3FZktQsjS0XUFv37q0wbPKZZ+DQQ+GrX4UXX4Qrr8wW5v7e\n96BTpyJ/WftmgJMkqY6IOCkiHgCeALYFzkgp9U8pnZlSmp3H+TdExJKIeLZW2+YRcV9EzM29b9aK\nP4IkrSOf5QC6dIErrijily5YAMOHw847w4MPZl178+fDqafCBhsU8Ys6DgOcJEnr2gu4BOidUjot\npfRwgef/DjigTttZwP0ppQHA/bltSWozTS0HsPHGRZy05PXXs5lSttsO/vhH+MlPsjB39tnQrVvT\n56tBBjhJkupIKR2fUvprSmltM89/EFhWp/kwYGru81Tg8BaUKEkFmzgROneuf9+oUfDuu0UIb2+/\nDWedBdtsA9deCyeemPW4XXopbL55Cy8ucBZKSZLaSs+U0usAKaXXI+JzpS5IUsdSE87GjPlkCYHu\n3bMhky0Obu+9l13osstg+XI45hj42c+yIKeiMsBJklRmImIkMBKgT1NjniSpAMOGFXmCko8+gilT\n4MIL4Y034OCDs66+nXYq4peoNodQSpJUR27CkQZfzbzsGxHRK3f9XsCShg5MKU1JKQ1OKQ3u0aNH\nM79Okj6tqgr69YP11sveq6pacLE1a+Cmm2D77eG007Jn3R56CP78Z8NbK7MHTpKkdT0OJCCAPsDb\nuc+bAouALzTjmjPIFgS/JPd+R1EqlaQ8VFXB97+fdZgBLFyYbUOBPXIpwYwZ2ZoEzz0Hu+4Kd98N\n++/vOm5txB44SZLqSCl9IaXUH7gXOCSltEVKqTtwMPCnps6PiJuBvwPbRcSrEXECWXDbNyLmAvvm\ntiWp1VRVZTNLRmQ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      "text/plain": [
       "<Figure size 1080x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     Survived  Pclass     Sex   Age  SibSp     Fare  Fare_square_root\n",
      "857         1       1    male  51.0      0  26.5500          5.152669\n",
      "52          1       1  female  49.0      1  76.7292          8.759521\n",
      "386         0       3    male   1.0      5  46.9000          6.848357\n",
      "124         0       1    male  54.0      0  77.2875          8.791331\n",
      "578         0       3  female   NaN      1  14.4583          3.802407\n",
      "549         1       2    male   8.0      1  36.7500          6.062178\n"
     ]
    }
   ],
   "source": [
    "# add the new created feature\n",
    "X_train_copy = X_train.copy(deep=True)\n",
    "X_train_copy = transformation.square_root_transform(data=X_train,cols=['Fare'])\n",
    "print(X_train_copy.head(6))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exponential transformation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Variable Fare Q-Q plot\n"
     ]
    },
    {
     "data": {
      "image/png": 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fLDVbEXix3semlY5JktTpRcSAiLglIh4tvV87Io6odl2SOonnnoOttoIf/pD3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\n",
      "text/plain": [
       "<Figure size 1080x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     Survived  Pclass     Sex   Age  SibSp     Fare  Fare_exp\n",
      "857         1       1    male  51.0      0  26.5500  1.926695\n",
      "52          1       1  female  49.0      1  76.7292  2.382276\n",
      "386         0       3    male   1.0      5  46.9000  2.158910\n",
      "124         0       1    male  54.0      0  77.2875  2.385733\n",
      "578         0       3  female   NaN      1  14.4583  1.706174\n",
      "549         1       2    male   8.0      1  36.7500  2.056134\n"
     ]
    }
   ],
   "source": [
    "# add the new created feature\n",
    "X_train_copy = X_train.copy(deep=True)\n",
    "X_train_copy = transformation.exp_transform(data=X_train,cols=['Fare'],coef=0.2)\n",
    "print(X_train_copy.head(6))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Box-cox transformation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     Survived  Pclass     Sex   Age  SibSp     Fare  Fare_boxcox\n",
      "857         1       1    male  51.0      0  26.5500     0.398696\n",
      "52          1       1  female  49.0      1  76.7292     1.401774\n",
      "386         0       3    male   1.0      5  46.9000     0.950718\n",
      "124         0       1    male  54.0      0  77.2875     1.408228\n",
      "578         0       3  female   NaN      1  14.4583    -0.222897\n",
      "549         1       2    male   8.0      1  36.7500     0.718007\n"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import PowerTransformer\n",
    "pt = PowerTransformer().fit(X_train[['Fare']])\n",
    "X_train_copy = X_train.copy(deep=True)\n",
    "X_train_copy['Fare_boxcox'] = pt.transform(X_train_copy[['Fare']])\n",
    "print(X_train_copy.head(6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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PA7sAJwGklF4E/gi8BPwZODaltCRv1UuSlB9/AM4AFgGklJ4nm2VZUhtQUQGz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\nZmZmZlYSbuDMzMzMzMxK4r+4mbuoGH9rRQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1080x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "transformation.diagnostic_plots(X_train_copy,'Fare_boxcox')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Quantile transformation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     Survived  Pclass     Sex   Age  SibSp     Fare   Fare_qt\n",
      "857         1       1    male  51.0      0  26.5500  0.468203\n",
      "52          1       1  female  49.0      1  76.7292  1.269664\n",
      "386         0       3    male   1.0      5  46.9000  0.840906\n",
      "124         0       1    male  54.0      0  77.2875  1.289578\n",
      "578         0       3  female   NaN      1  14.4583 -0.023839\n",
      "549         1       2    male   8.0      1  36.7500  0.744646\n"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import QuantileTransformer\n",
    "qt = QuantileTransformer(output_distribution='normal').fit(X_train[['Fare']])\n",
    "X_train_copy = X_train.copy(deep=True)\n",
    "X_train_copy['Fare_qt'] = qt.transform(X_train_copy[['Fare']])\n",
    "print(X_train_copy.head(6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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6NaVy0rUrzJz55W1r8DancBr/xxV8wooMZRS/5Vd8xgr1nsMRN1UyA5wkSa0k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\n",
      "text/plain": [
       "<Figure size 1080x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "transformation.diagnostic_plots(X_train_copy,'Fare_qt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
